Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations500
Missing cells376
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory548.8 KiB
Average record size in memory1.1 KiB

Variable types

Numeric13
Text1
Categorical13
DateTime3
Boolean3

Alerts

Absenteeism_Days is highly overall correlated with Absenteeism_vs_CompanyHigh correlation
Absenteeism_vs_Company is highly overall correlated with Absenteeism_DaysHigh correlation
Attrition_Status is highly overall correlated with Reason_for_Leaving and 2 other fieldsHigh correlation
Average_Monthly_Work_Hours is highly overall correlated with Burnout_Risk and 1 other fieldsHigh correlation
Burnout_Risk is highly overall correlated with Average_Monthly_Work_Hours and 1 other fieldsHigh correlation
Engagement_Change is highly overall correlated with Yearly_EngagementHigh correlation
Engagement_Score is highly overall correlated with Engagement_vs_CompanyHigh correlation
Engagement_vs_Company is highly overall correlated with Engagement_ScoreHigh correlation
Job_Role is highly overall correlated with Salary_LevelHigh correlation
Performance_Rating is highly overall correlated with Performance_vs_CompanyHigh correlation
Performance_vs_Company is highly overall correlated with Performance_RatingHigh correlation
Reason_for_Leaving is highly overall correlated with Attrition_Status and 1 other fieldsHigh correlation
Salary_Level is highly overall correlated with Job_RoleHigh correlation
Still_Employed is highly overall correlated with Attrition_Status and 2 other fieldsHigh correlation
Tenure_at_Company is highly overall correlated with Attrition_Status and 1 other fieldsHigh correlation
Yearly_Engagement is highly overall correlated with Engagement_ChangeHigh correlation
Yearly_Work_Hours is highly overall correlated with Average_Monthly_Work_Hours and 1 other fieldsHigh correlation
Leaving_Date has 376 (75.2%) missing values Missing
Employee_ID is uniformly distributed Uniform
Branch is uniformly distributed Uniform
Employee_ID has unique values Unique
Employe_Name has unique values Unique
Absenteeism_Days has 17 (3.4%) zeros Zeros
Attended_Training_Hours has 13 (2.6%) zeros Zeros
Absenteeism_Training_Hours has 70 (14.0%) zeros Zeros

Reproduction

Analysis started2025-07-09 07:14:28.335259
Analysis finished2025-07-09 07:14:52.543131
Duration24.21 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Employee_ID
Real number (ℝ)

Uniform  Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:52.668265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2025-07-09T07:14:52.848803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 1
 
0.2%
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
484 1
 
0.2%
483 1
 
0.2%
482 1
 
0.2%
481 1
 
0.2%
480 1
 
0.2%
479 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

Employe_Name
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2025-07-09T07:14:53.234003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length25
Mean length14.836
Min length8

Characters and Unicode

Total characters7418
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowMiss Bertha Bradtke
2nd rowDixie Erdman
3rd rowAndrea Langworth
4th rowWendell Barton
5th rowNicholas Kuhn
ValueCountFrequency (%)
dr 26
 
2.3%
mr 18
 
1.6%
ii 9
 
0.8%
ms 9
 
0.8%
phd 8
 
0.7%
md 8
 
0.7%
i 7
 
0.6%
iii 7
 
0.6%
mrs 7
 
0.6%
iv 5
 
0.4%
Other values (702) 1021
90.8%
2025-07-09T07:14:53.718384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 731
 
9.9%
625
 
8.4%
a 577
 
7.8%
r 560
 
7.5%
n 513
 
6.9%
i 413
 
5.6%
o 402
 
5.4%
l 388
 
5.2%
t 245
 
3.3%
s 241
 
3.2%
Other values (43) 2723
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 731
 
9.9%
625
 
8.4%
a 577
 
7.8%
r 560
 
7.5%
n 513
 
6.9%
i 413
 
5.6%
o 402
 
5.4%
l 388
 
5.2%
t 245
 
3.3%
s 241
 
3.2%
Other values (43) 2723
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 731
 
9.9%
625
 
8.4%
a 577
 
7.8%
r 560
 
7.5%
n 513
 
6.9%
i 413
 
5.6%
o 402
 
5.4%
l 388
 
5.2%
t 245
 
3.3%
s 241
 
3.2%
Other values (43) 2723
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 731
 
9.9%
625
 
8.4%
a 577
 
7.8%
r 560
 
7.5%
n 513
 
6.9%
i 413
 
5.6%
o 402
 
5.4%
l 388
 
5.2%
t 245
 
3.3%
s 241
 
3.2%
Other values (43) 2723
36.7%

Gender
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size30.4 KiB
Female
252 
Male
248 

Length

Max length6
Median length6
Mean length5.008
Min length4

Characters and Unicode

Total characters2504
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 252
50.4%
Male 248
49.6%

Length

2025-07-09T07:14:53.850498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:53.937069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 252
50.4%
male 248
49.6%

Most occurring characters

ValueCountFrequency (%)
e 752
30.0%
a 500
20.0%
l 500
20.0%
F 252
 
10.1%
m 252
 
10.1%
M 248
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 752
30.0%
a 500
20.0%
l 500
20.0%
F 252
 
10.1%
m 252
 
10.1%
M 248
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 752
30.0%
a 500
20.0%
l 500
20.0%
F 252
 
10.1%
m 252
 
10.1%
M 248
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 752
30.0%
a 500
20.0%
l 500
20.0%
F 252
 
10.1%
m 252
 
10.1%
M 248
 
9.9%

Age
Real number (ℝ)

Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.448
Minimum22
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:54.030387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23
Q132
median40
Q350
95-th percentile57
Maximum59
Range37
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.876944
Coefficient of variation (CV)0.26891179
Kurtosis-1.1228765
Mean40.448
Median Absolute Deviation (MAD)9
Skewness-0.054442002
Sum20224
Variance118.30791
MonotonicityNot monotonic
2025-07-09T07:14:54.159266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
22 22
 
4.4%
45 19
 
3.8%
40 19
 
3.8%
38 19
 
3.8%
34 17
 
3.4%
50 17
 
3.4%
33 17
 
3.4%
44 17
 
3.4%
35 16
 
3.2%
57 16
 
3.2%
Other values (28) 321
64.2%
ValueCountFrequency (%)
22 22
4.4%
23 14
2.8%
24 14
2.8%
25 8
 
1.6%
26 10
2.0%
27 11
2.2%
28 10
2.0%
29 14
2.8%
30 9
1.8%
31 11
2.2%
ValueCountFrequency (%)
59 11
2.2%
58 10
2.0%
57 16
3.2%
56 13
2.6%
55 12
2.4%
54 11
2.2%
53 14
2.8%
52 12
2.4%
51 13
2.6%
50 17
3.4%
Distinct492
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum1965-02-12 00:00:00
Maximum2003-10-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-09T07:14:54.297725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:54.445260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Branch
Categorical

Uniform 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size32.8 KiB
United_Kingdom
125 
Singapore
125 
India
125 
South_Africa
125 

Length

Max length14
Median length10.5
Mean length10
Min length5

Characters and Unicode

Total characters5000
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited_Kingdom
2nd rowSingapore
3rd rowIndia
4th rowSouth_Africa
5th rowUnited_Kingdom

Common Values

ValueCountFrequency (%)
United_Kingdom 125
25.0%
Singapore 125
25.0%
India 125
25.0%
South_Africa 125
25.0%

Length

2025-07-09T07:14:54.571807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:54.642545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united_kingdom 125
25.0%
singapore 125
25.0%
india 125
25.0%
south_africa 125
25.0%

Most occurring characters

ValueCountFrequency (%)
i 625
12.5%
n 500
 
10.0%
a 375
 
7.5%
o 375
 
7.5%
d 375
 
7.5%
S 250
 
5.0%
e 250
 
5.0%
t 250
 
5.0%
_ 250
 
5.0%
r 250
 
5.0%
Other values (11) 1500
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 625
12.5%
n 500
 
10.0%
a 375
 
7.5%
o 375
 
7.5%
d 375
 
7.5%
S 250
 
5.0%
e 250
 
5.0%
t 250
 
5.0%
_ 250
 
5.0%
r 250
 
5.0%
Other values (11) 1500
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 625
12.5%
n 500
 
10.0%
a 375
 
7.5%
o 375
 
7.5%
d 375
 
7.5%
S 250
 
5.0%
e 250
 
5.0%
t 250
 
5.0%
_ 250
 
5.0%
r 250
 
5.0%
Other values (11) 1500
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 625
12.5%
n 500
 
10.0%
a 375
 
7.5%
o 375
 
7.5%
d 375
 
7.5%
S 250
 
5.0%
e 250
 
5.0%
t 250
 
5.0%
_ 250
 
5.0%
r 250
 
5.0%
Other values (11) 1500
30.0%

Department
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
Technical
94 
Management
88 
Finance
87 
Sales
82 
Support
76 

Length

Max length10
Median length9
Mean length6.846
Min length2

Characters and Unicode

Total characters3423
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupport
2nd rowTechnical
3rd rowTechnical
4th rowTechnical
5th rowSales

Common Values

ValueCountFrequency (%)
Technical 94
18.8%
Management 88
17.6%
Finance 87
17.4%
Sales 82
16.4%
Support 76
15.2%
IT 73
14.6%

Length

2025-07-09T07:14:54.779552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:54.897607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
technical 94
18.8%
management 88
17.6%
finance 87
17.4%
sales 82
16.4%
support 76
15.2%
it 73
14.6%

Most occurring characters

ValueCountFrequency (%)
n 444
13.0%
e 439
12.8%
a 439
12.8%
c 275
 
8.0%
i 181
 
5.3%
l 176
 
5.1%
T 167
 
4.9%
t 164
 
4.8%
S 158
 
4.6%
p 152
 
4.4%
Other values (10) 828
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3423
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 444
13.0%
e 439
12.8%
a 439
12.8%
c 275
 
8.0%
i 181
 
5.3%
l 176
 
5.1%
T 167
 
4.9%
t 164
 
4.8%
S 158
 
4.6%
p 152
 
4.4%
Other values (10) 828
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3423
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 444
13.0%
e 439
12.8%
a 439
12.8%
c 275
 
8.0%
i 181
 
5.3%
l 176
 
5.1%
T 167
 
4.9%
t 164
 
4.8%
S 158
 
4.6%
p 152
 
4.4%
Other values (10) 828
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3423
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 444
13.0%
e 439
12.8%
a 439
12.8%
c 275
 
8.0%
i 181
 
5.3%
l 176
 
5.1%
T 167
 
4.9%
t 164
 
4.8%
S 158
 
4.6%
p 152
 
4.4%
Other values (10) 828
24.2%

Job_Role
Categorical

High correlation 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size31.5 KiB
Engineer
111 
Executive
103 
Analyst
101 
Clerk
98 
Manager
87 

Length

Max length9
Median length8
Mean length7.242
Min length5

Characters and Unicode

Total characters3621
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngineer
2nd rowAnalyst
3rd rowExecutive
4th rowManager
5th rowClerk

Common Values

ValueCountFrequency (%)
Engineer 111
22.2%
Executive 103
20.6%
Analyst 101
20.2%
Clerk 98
19.6%
Manager 87
17.4%

Length

2025-07-09T07:14:55.021943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:55.113848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
engineer 111
22.2%
executive 103
20.6%
analyst 101
20.2%
clerk 98
19.6%
manager 87
17.4%

Most occurring characters

ValueCountFrequency (%)
e 613
16.9%
n 410
11.3%
r 296
 
8.2%
a 275
 
7.6%
E 214
 
5.9%
i 214
 
5.9%
t 204
 
5.6%
l 199
 
5.5%
g 198
 
5.5%
x 103
 
2.8%
Other values (9) 895
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 613
16.9%
n 410
11.3%
r 296
 
8.2%
a 275
 
7.6%
E 214
 
5.9%
i 214
 
5.9%
t 204
 
5.6%
l 199
 
5.5%
g 198
 
5.5%
x 103
 
2.8%
Other values (9) 895
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 613
16.9%
n 410
11.3%
r 296
 
8.2%
a 275
 
7.6%
E 214
 
5.9%
i 214
 
5.9%
t 204
 
5.6%
l 199
 
5.5%
g 198
 
5.5%
x 103
 
2.8%
Other values (9) 895
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 613
16.9%
n 410
11.3%
r 296
 
8.2%
a 275
 
7.6%
E 214
 
5.9%
i 214
 
5.9%
t 204
 
5.6%
l 199
 
5.5%
g 198
 
5.5%
x 103
 
2.8%
Other values (9) 895
24.7%

Salary_Level
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
Medium
212 
High
190 
Low
98 

Length

Max length6
Median length4
Mean length4.652
Min length3

Characters and Unicode

Total characters2326
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowHigh
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 212
42.4%
High 190
38.0%
Low 98
19.6%

Length

2025-07-09T07:14:55.228709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:55.300951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 212
42.4%
high 190
38.0%
low 98
19.6%

Most occurring characters

ValueCountFrequency (%)
i 402
17.3%
M 212
9.1%
e 212
9.1%
d 212
9.1%
u 212
9.1%
m 212
9.1%
H 190
8.2%
g 190
8.2%
h 190
8.2%
L 98
 
4.2%
Other values (2) 196
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 402
17.3%
M 212
9.1%
e 212
9.1%
d 212
9.1%
u 212
9.1%
m 212
9.1%
H 190
8.2%
g 190
8.2%
h 190
8.2%
L 98
 
4.2%
Other values (2) 196
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 402
17.3%
M 212
9.1%
e 212
9.1%
d 212
9.1%
u 212
9.1%
m 212
9.1%
H 190
8.2%
g 190
8.2%
h 190
8.2%
L 98
 
4.2%
Other values (2) 196
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 402
17.3%
M 212
9.1%
e 212
9.1%
d 212
9.1%
u 212
9.1%
m 212
9.1%
H 190
8.2%
g 190
8.2%
h 190
8.2%
L 98
 
4.2%
Other values (2) 196
8.4%

Work_Location
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
On Site
176 
Remote
169 
Work From Home
155 

Length

Max length14
Median length7
Mean length8.832
Min length6

Characters and Unicode

Total characters4416
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWork From Home
2nd rowRemote
3rd rowOn Site
4th rowOn Site
5th rowWork From Home

Common Values

ValueCountFrequency (%)
On Site 176
35.2%
Remote 169
33.8%
Work From Home 155
31.0%

Length

2025-07-09T07:14:55.403662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:55.485805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
on 176
17.8%
site 176
17.8%
remote 169
17.1%
work 155
15.7%
from 155
15.7%
home 155
15.7%

Most occurring characters

ValueCountFrequency (%)
e 669
15.1%
o 634
14.4%
486
11.0%
m 479
10.8%
t 345
7.8%
r 310
 
7.0%
n 176
 
4.0%
S 176
 
4.0%
i 176
 
4.0%
O 176
 
4.0%
Other values (5) 789
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 669
15.1%
o 634
14.4%
486
11.0%
m 479
10.8%
t 345
7.8%
r 310
 
7.0%
n 176
 
4.0%
S 176
 
4.0%
i 176
 
4.0%
O 176
 
4.0%
Other values (5) 789
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 669
15.1%
o 634
14.4%
486
11.0%
m 479
10.8%
t 345
7.8%
r 310
 
7.0%
n 176
 
4.0%
S 176
 
4.0%
i 176
 
4.0%
O 176
 
4.0%
Other values (5) 789
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 669
15.1%
o 634
14.4%
486
11.0%
m 479
10.8%
t 345
7.8%
r 310
 
7.0%
n 176
 
4.0%
S 176
 
4.0%
i 176
 
4.0%
O 176
 
4.0%
Other values (5) 789
17.9%
Distinct481
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2000-01-17 00:00:00
Maximum2019-12-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-09T07:14:55.591671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:55.747860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Leaving_Date
Date

Missing 

Distinct123
Distinct (%)99.2%
Missing376
Missing (%)75.2%
Memory size4.0 KiB
Minimum2001-06-28 00:00:00
Maximum2022-11-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-09T07:14:55.929696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:56.086386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tenure_at_Company
Categorical

High correlation 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
0
47 
1
39 
2
35 
15
 
26
6
 
23
Other values (20)
330 

Length

Max length5
Median length2
Mean length1.57
Min length1

Characters and Unicode

Total characters785
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row10
2nd row22
3rd row17
4th row8
5th row2

Common Values

ValueCountFrequency (%)
0 47
 
9.4%
1 39
 
7.8%
2 35
 
7.0%
15 26
 
5.2%
6 23
 
4.6%
7 23
 
4.6%
16 22
 
4.4%
24 22
 
4.4%
13 22
 
4.4%
10 21
 
4.2%
Other values (15) 220
44.0%

Length

2025-07-09T07:14:56.230984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 47
 
9.4%
1 39
 
7.8%
2 35
 
7.0%
15 26
 
5.2%
6 23
 
4.6%
7 23
 
4.6%
16 22
 
4.4%
24 22
 
4.4%
13 22
 
4.4%
10 21
 
4.2%
Other values (15) 220
44.0%

Most occurring characters

ValueCountFrequency (%)
1 266
33.9%
2 157
20.0%
0 78
 
9.9%
6 45
 
5.7%
7 41
 
5.2%
4 41
 
5.2%
3 41
 
5.2%
9 39
 
5.0%
5 38
 
4.8%
8 34
 
4.3%
Other values (5) 5
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 266
33.9%
2 157
20.0%
0 78
 
9.9%
6 45
 
5.7%
7 41
 
5.2%
4 41
 
5.2%
3 41
 
5.2%
9 39
 
5.0%
5 38
 
4.8%
8 34
 
4.3%
Other values (5) 5
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 266
33.9%
2 157
20.0%
0 78
 
9.9%
6 45
 
5.7%
7 41
 
5.2%
4 41
 
5.2%
3 41
 
5.2%
9 39
 
5.0%
5 38
 
4.8%
8 34
 
4.3%
Other values (5) 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 266
33.9%
2 157
20.0%
0 78
 
9.9%
6 45
 
5.7%
7 41
 
5.2%
4 41
 
5.2%
3 41
 
5.2%
9 39
 
5.0%
5 38
 
4.8%
8 34
 
4.3%
Other values (5) 5
 
0.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
258 
False
242 
ValueCountFrequency (%)
True 258
51.6%
False 242
48.4%
2025-07-09T07:14:56.302548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Still_Employed
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
376 
False
124 
ValueCountFrequency (%)
True 376
75.2%
False 124
 
24.8%
2025-07-09T07:14:56.354235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Reason_for_Leaving
Categorical

High correlation 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
376 
Work Environment
 
25
Burn Out
 
23
Financial Problem
 
21
Health Issues
 
20
Other values (2)
 
35

Length

Max length17
Median length1
Mean length3.882
Min length1

Characters and Unicode

Total characters1941
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th rowHealth Issues

Common Values

ValueCountFrequency (%)
376
75.2%
Work Environment 25
 
5.0%
Burn Out 23
 
4.6%
Financial Problem 21
 
4.2%
Health Issues 20
 
4.0%
Low Pay 18
 
3.6%
Personal Issue 17
 
3.4%

Length

2025-07-09T07:14:56.447296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:56.539842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
work 25
10.1%
environment 25
10.1%
burn 23
9.3%
out 23
9.3%
financial 21
8.5%
problem 21
8.5%
health 20
8.1%
issues 20
8.1%
low 18
7.3%
pay 18
7.3%
Other values (2) 34
13.7%

Most occurring characters

ValueCountFrequency (%)
500
25.8%
n 157
 
8.1%
e 120
 
6.2%
r 111
 
5.7%
s 111
 
5.7%
o 106
 
5.5%
a 97
 
5.0%
u 83
 
4.3%
l 79
 
4.1%
t 68
 
3.5%
Other values (18) 509
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
500
25.8%
n 157
 
8.1%
e 120
 
6.2%
r 111
 
5.7%
s 111
 
5.7%
o 106
 
5.5%
a 97
 
5.0%
u 83
 
4.3%
l 79
 
4.1%
t 68
 
3.5%
Other values (18) 509
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
500
25.8%
n 157
 
8.1%
e 120
 
6.2%
r 111
 
5.7%
s 111
 
5.7%
o 106
 
5.5%
a 97
 
5.0%
u 83
 
4.3%
l 79
 
4.1%
t 68
 
3.5%
Other values (18) 509
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
500
25.8%
n 157
 
8.1%
e 120
 
6.2%
r 111
 
5.7%
s 111
 
5.7%
o 106
 
5.5%
a 97
 
5.0%
u 83
 
4.3%
l 79
 
4.1%
t 68
 
3.5%
Other values (18) 509
26.2%

Satisfaction_Level
Real number (ℝ)

Distinct100
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50124
Minimum0
Maximum1
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:56.668191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.26
median0.51
Q30.7125
95-th percentile0.94
Maximum1
Range1
Interquartile range (IQR)0.4525

Descriptive statistics

Standard deviation0.27314186
Coefficient of variation (CV)0.54493229
Kurtosis-1.0791708
Mean0.50124
Median Absolute Deviation (MAD)0.22
Skewness-0.029137125
Sum250.62
Variance0.074606475
MonotonicityNot monotonic
2025-07-09T07:14:56.852277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66 11
 
2.2%
0.24 11
 
2.2%
0.72 9
 
1.8%
0.68 9
 
1.8%
0.36 9
 
1.8%
0.64 8
 
1.6%
0.53 8
 
1.6%
0.25 8
 
1.6%
0.48 8
 
1.6%
0.54 8
 
1.6%
Other values (90) 411
82.2%
ValueCountFrequency (%)
0 2
 
0.4%
0.01 2
 
0.4%
0.03 7
1.4%
0.04 4
0.8%
0.05 4
0.8%
0.06 7
1.4%
0.07 4
0.8%
0.08 8
1.6%
0.09 2
 
0.4%
0.1 6
1.2%
ValueCountFrequency (%)
1 4
0.8%
0.99 4
0.8%
0.98 2
 
0.4%
0.97 5
1.0%
0.96 5
1.0%
0.95 4
0.8%
0.94 5
1.0%
0.93 4
0.8%
0.92 2
 
0.4%
0.91 1
 
0.2%

Engagement_Score
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49354
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:56.996923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.25
median0.495
Q30.74
95-th percentile0.94
Maximum1
Range1
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.28572109
Coefficient of variation (CV)0.57892185
Kurtosis-1.1911499
Mean0.49354
Median Absolute Deviation (MAD)0.245
Skewness0.022116545
Sum246.77
Variance0.081636541
MonotonicityNot monotonic
2025-07-09T07:14:57.151327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 10
 
2.0%
0.62 9
 
1.8%
0.4 9
 
1.8%
0.39 9
 
1.8%
0.08 9
 
1.8%
0.29 8
 
1.6%
0.12 8
 
1.6%
0.42 8
 
1.6%
0.58 8
 
1.6%
0.05 8
 
1.6%
Other values (91) 414
82.8%
ValueCountFrequency (%)
0 1
 
0.2%
0.01 6
1.2%
0.02 2
 
0.4%
0.03 5
1.0%
0.04 2
 
0.4%
0.05 8
1.6%
0.06 7
1.4%
0.07 7
1.4%
0.08 9
1.8%
0.09 2
 
0.4%
ValueCountFrequency (%)
1 2
 
0.4%
0.99 2
 
0.4%
0.98 4
0.8%
0.97 3
 
0.6%
0.96 4
0.8%
0.95 5
1.0%
0.94 8
1.6%
0.93 3
 
0.6%
0.92 4
0.8%
0.91 7
1.4%

Performance_Rating
Categorical

High correlation 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
113 
3
105 
4
104 
5
93 
2
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Length

2025-07-09T07:14:57.277698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:57.356524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Most occurring characters

ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 113
22.6%
3 105
21.0%
4 104
20.8%
5 93
18.6%
2 85
17.0%

Average_Monthly_Work_Hours
Real number (ℝ)

High correlation 

Distinct135
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.654
Minimum120
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:57.471401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile127
Q1156.75
median191
Q3225.25
95-th percentile250.05
Maximum259
Range139
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation39.421598
Coefficient of variation (CV)0.20677037
Kurtosis-1.1770305
Mean190.654
Median Absolute Deviation (MAD)34.5
Skewness-0.051597191
Sum95327
Variance1554.0624
MonotonicityNot monotonic
2025-07-09T07:14:57.613527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191 9
 
1.8%
148 8
 
1.6%
200 8
 
1.6%
231 7
 
1.4%
179 7
 
1.4%
127 7
 
1.4%
253 7
 
1.4%
123 6
 
1.2%
244 6
 
1.2%
206 6
 
1.2%
Other values (125) 429
85.8%
ValueCountFrequency (%)
120 3
0.6%
121 4
0.8%
123 6
1.2%
124 1
 
0.2%
125 2
 
0.4%
126 4
0.8%
127 7
1.4%
128 4
0.8%
129 2
 
0.4%
130 4
0.8%
ValueCountFrequency (%)
259 1
 
0.2%
258 4
0.8%
257 4
0.8%
256 1
 
0.2%
255 1
 
0.2%
254 1
 
0.2%
253 7
1.4%
252 1
 
0.2%
251 5
1.0%
250 5
1.0%

Absenteeism_Days
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.168
Minimum0
Maximum30
Zeros17
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:57.752841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median15
Q323
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8682054
Coefficient of variation (CV)0.58466544
Kurtosis-1.1802906
Mean15.168
Median Absolute Deviation (MAD)8
Skewness-0.010729833
Sum7584
Variance78.645066
MonotonicityNot monotonic
2025-07-09T07:14:58.374178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25 23
 
4.6%
12 22
 
4.4%
8 20
 
4.0%
16 19
 
3.8%
15 19
 
3.8%
23 18
 
3.6%
30 18
 
3.6%
22 18
 
3.6%
21 18
 
3.6%
14 17
 
3.4%
Other values (21) 308
61.6%
ValueCountFrequency (%)
0 17
3.4%
1 11
2.2%
2 17
3.4%
3 17
3.4%
4 12
2.4%
5 16
3.2%
6 14
2.8%
7 17
3.4%
8 20
4.0%
9 15
3.0%
ValueCountFrequency (%)
30 18
3.6%
29 15
3.0%
28 16
3.2%
27 13
2.6%
26 14
2.8%
25 23
4.6%
24 16
3.2%
23 18
3.6%
22 18
3.6%
21 18
3.6%

Burnout_Risk
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Low
282 
Medium
153 
High
65 

Length

Max length6
Median length3
Mean length4.048
Min length3

Characters and Unicode

Total characters2024
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low 282
56.4%
Medium 153
30.6%
High 65
 
13.0%

Length

2025-07-09T07:14:58.539662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:58.642648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 282
56.4%
medium 153
30.6%
high 65
 
13.0%

Most occurring characters

ValueCountFrequency (%)
L 282
13.9%
o 282
13.9%
w 282
13.9%
i 218
10.8%
M 153
7.6%
e 153
7.6%
d 153
7.6%
u 153
7.6%
m 153
7.6%
H 65
 
3.2%
Other values (2) 130
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 282
13.9%
o 282
13.9%
w 282
13.9%
i 218
10.8%
M 153
7.6%
e 153
7.6%
d 153
7.6%
u 153
7.6%
m 153
7.6%
H 65
 
3.2%
Other values (2) 130
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 282
13.9%
o 282
13.9%
w 282
13.9%
i 218
10.8%
M 153
7.6%
e 153
7.6%
d 153
7.6%
u 153
7.6%
m 153
7.6%
H 65
 
3.2%
Other values (2) 130
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 282
13.9%
o 282
13.9%
w 282
13.9%
i 218
10.8%
M 153
7.6%
e 153
7.6%
d 153
7.6%
u 153
7.6%
m 153
7.6%
H 65
 
3.2%
Other values (2) 130
6.4%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
107 
3
106 
4
106 
5
97 
2
84 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Length

2025-07-09T07:14:58.763766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:58.876521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Most occurring characters

ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 107
21.4%
3 106
21.2%
4 106
21.2%
5 97
19.4%
2 84
16.8%

Yearly_Engagement
Categorical

High correlation 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
5
119 
4
102 
1
97 
2
92 
3
90 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Length

2025-07-09T07:14:59.027865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:14:59.131919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Most occurring characters

ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 119
23.8%
4 102
20.4%
1 97
19.4%
2 92
18.4%
3 90
18.0%

Yearly_Work_Hours
Real number (ℝ)

High correlation 

Distinct135
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2287.848
Minimum1440
Maximum3108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:14:59.308369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1440
5-th percentile1524
Q11881
median2292
Q32703
95-th percentile3000.6
Maximum3108
Range1668
Interquartile range (IQR)822

Descriptive statistics

Standard deviation473.05918
Coefficient of variation (CV)0.20677037
Kurtosis-1.1770305
Mean2287.848
Median Absolute Deviation (MAD)414
Skewness-0.051597191
Sum1143924
Variance223784.99
MonotonicityNot monotonic
2025-07-09T07:14:59.507034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2292 9
 
1.8%
1776 8
 
1.6%
2400 8
 
1.6%
2772 7
 
1.4%
2148 7
 
1.4%
1524 7
 
1.4%
3036 7
 
1.4%
1476 6
 
1.2%
2928 6
 
1.2%
2472 6
 
1.2%
Other values (125) 429
85.8%
ValueCountFrequency (%)
1440 3
0.6%
1452 4
0.8%
1476 6
1.2%
1488 1
 
0.2%
1500 2
 
0.4%
1512 4
0.8%
1524 7
1.4%
1536 4
0.8%
1548 2
 
0.4%
1560 4
0.8%
ValueCountFrequency (%)
3108 1
 
0.2%
3096 4
0.8%
3084 4
0.8%
3072 1
 
0.2%
3060 1
 
0.2%
3048 1
 
0.2%
3036 7
1.4%
3024 1
 
0.2%
3012 5
1.0%
3000 5
1.0%

Engagement_vs_Company
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9920072 × 10-19
Minimum-0.49354
Maximum0.50646
Zeros0
Zeros (%)0.0%
Negative250
Negative (%)50.0%
Memory size4.0 KiB
2025-07-09T07:14:59.694452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.49354
5-th percentile-0.43354
Q1-0.24354
median0.00146
Q30.24646
95-th percentile0.44646
Maximum0.50646
Range1
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.28572109
Coefficient of variation (CV)2.8594964 × 1017
Kurtosis-1.1911499
Mean9.9920072 × 10-19
Median Absolute Deviation (MAD)0.245
Skewness0.022116545
Sum-3.9412917 × 10-15
Variance0.081636541
MonotonicityNot monotonic
2025-07-09T07:14:59.892939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.40646 10
 
2.0%
0.12646 9
 
1.8%
-0.09354 9
 
1.8%
-0.10354 9
 
1.8%
-0.41354 9
 
1.8%
-0.20354 8
 
1.6%
-0.37354 8
 
1.6%
-0.07354 8
 
1.6%
0.08646 8
 
1.6%
-0.44354 8
 
1.6%
Other values (91) 414
82.8%
ValueCountFrequency (%)
-0.49354 1
 
0.2%
-0.48354 6
1.2%
-0.47354 2
 
0.4%
-0.46354 5
1.0%
-0.45354 2
 
0.4%
-0.44354 8
1.6%
-0.43354 7
1.4%
-0.42354 7
1.4%
-0.41354 9
1.8%
-0.40354 2
 
0.4%
ValueCountFrequency (%)
0.50646 2
 
0.4%
0.49646 2
 
0.4%
0.48646 4
0.8%
0.47646 3
 
0.6%
0.46646 4
0.8%
0.45646 5
1.0%
0.44646 8
1.6%
0.43646 3
 
0.6%
0.42646 4
0.8%
0.41646 7
1.4%

Absenteeism_vs_Company
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8369308 × 10-16
Minimum-15.168
Maximum14.832
Zeros0
Zeros (%)0.0%
Negative258
Negative (%)51.6%
Memory size4.0 KiB
2025-07-09T07:15:00.119370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15.168
5-th percentile-14.168
Q1-7.168
median-0.168
Q37.832
95-th percentile13.832
Maximum14.832
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8682054
Coefficient of variation (CV)2.3112758 × 1016
Kurtosis-1.1802906
Mean3.8369308 × 10-16
Median Absolute Deviation (MAD)8
Skewness-0.010729833
Sum1.9184654 × 10-13
Variance78.645066
MonotonicityNot monotonic
2025-07-09T07:15:00.278409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9.832 23
 
4.6%
-3.168 22
 
4.4%
-7.168 20
 
4.0%
0.832 19
 
3.8%
-0.168 19
 
3.8%
7.832 18
 
3.6%
14.832 18
 
3.6%
6.832 18
 
3.6%
5.832 18
 
3.6%
-1.168 17
 
3.4%
Other values (21) 308
61.6%
ValueCountFrequency (%)
-15.168 17
3.4%
-14.168 11
2.2%
-13.168 17
3.4%
-12.168 17
3.4%
-11.168 12
2.4%
-10.168 16
3.2%
-9.168 14
2.8%
-8.168 17
3.4%
-7.168 20
4.0%
-6.168 15
3.0%
ValueCountFrequency (%)
14.832 18
3.6%
13.832 15
3.0%
12.832 16
3.2%
11.832 13
2.6%
10.832 14
2.8%
9.832 23
4.6%
8.832 16
3.2%
7.832 18
3.6%
6.832 18
3.6%
5.832 18
3.6%

Performance_vs_Company
Categorical

High correlation 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size30.6 KiB
-1.958
113 
0.042
105 
1.042
104 
2.042
93 
-0.958
85 

Length

Max length6
Median length5
Mean length5.396
Min length5

Characters and Unicode

Total characters2698
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.042
2nd row-0.958
3rd row-0.958
4th row-1.958
5th row1.042

Common Values

ValueCountFrequency (%)
-1.958 113
22.6%
0.042 105
21.0%
1.042 104
20.8%
2.042 93
18.6%
-0.958 85
17.0%

Length

2025-07-09T07:15:00.457087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T07:15:00.585176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.958 113
22.6%
0.042 105
21.0%
1.042 104
20.8%
2.042 93
18.6%
0.958 85
17.0%

Most occurring characters

ValueCountFrequency (%)
. 500
18.5%
0 492
18.2%
2 395
14.6%
4 302
11.2%
1 217
8.0%
- 198
 
7.3%
9 198
 
7.3%
5 198
 
7.3%
8 198
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 500
18.5%
0 492
18.2%
2 395
14.6%
4 302
11.2%
1 217
8.0%
- 198
 
7.3%
9 198
 
7.3%
5 198
 
7.3%
8 198
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 500
18.5%
0 492
18.2%
2 395
14.6%
4 302
11.2%
1 217
8.0%
- 198
 
7.3%
9 198
 
7.3%
5 198
 
7.3%
8 198
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 500
18.5%
0 492
18.2%
2 395
14.6%
4 302
11.2%
1 217
8.0%
- 198
 
7.3%
9 198
 
7.3%
5 198
 
7.3%
8 198
 
7.3%

Engagement_Change
Real number (ℝ)

High correlation 

Distinct321
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.61446
Minimum0
Maximum5
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:15:00.791039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2395
Q11.2875
median2.655
Q33.9425
95-th percentile4.7705
Maximum5
Range5
Interquartile range (IQR)2.655

Descriptive statistics

Standard deviation1.4847569
Coefficient of variation (CV)0.56790192
Kurtosis-1.2634532
Mean2.61446
Median Absolute Deviation (MAD)1.31
Skewness-0.10337244
Sum1307.23
Variance2.2045029
MonotonicityNot monotonic
2025-07-09T07:15:00.965534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.58 4
 
0.8%
1.16 4
 
0.8%
4.05 4
 
0.8%
4.5 4
 
0.8%
0.1 4
 
0.8%
3.71 4
 
0.8%
2.47 4
 
0.8%
3.14 4
 
0.8%
3.61 3
 
0.6%
0.41 3
 
0.6%
Other values (311) 462
92.4%
ValueCountFrequency (%)
0 1
 
0.2%
0.02 1
 
0.2%
0.03 2
0.4%
0.04 1
 
0.2%
0.05 1
 
0.2%
0.07 1
 
0.2%
0.09 3
0.6%
0.1 4
0.8%
0.11 1
 
0.2%
0.12 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4.99 1
 
0.2%
4.97 2
0.4%
4.95 1
 
0.2%
4.94 2
0.4%
4.93 3
0.6%
4.92 3
0.6%
4.91 1
 
0.2%
4.89 1
 
0.2%
4.88 2
0.4%

Total_Training_Hours
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.39
Minimum30
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:15:01.085519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q135
median45
Q355
95-th percentile60
Maximum60
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation10.5366
Coefficient of variation (CV)0.23213483
Kurtosis-1.3695092
Mean45.39
Median Absolute Deviation (MAD)10
Skewness-0.18700525
Sum22695
Variance111.01994
MonotonicityNot monotonic
2025-07-09T07:15:01.169134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50 94
18.8%
30 88
17.6%
35 87
17.4%
55 82
16.4%
45 76
15.2%
60 73
14.6%
ValueCountFrequency (%)
30 88
17.6%
35 87
17.4%
45 76
15.2%
50 94
18.8%
55 82
16.4%
60 73
14.6%
ValueCountFrequency (%)
60 73
14.6%
55 82
16.4%
50 94
18.8%
45 76
15.2%
35 87
17.4%
30 88
17.6%

Attended_Training_Hours
Real number (ℝ)

Zeros 

Distinct59
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.172
Minimum0
Maximum59
Zeros13
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:15:01.288273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median23
Q332
95-th percentile50
Maximum59
Range59
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.653049
Coefficient of variation (CV)0.63236013
Kurtosis-0.64979637
Mean23.172
Median Absolute Deviation (MAD)11
Skewness0.30901914
Sum11586
Variance214.71184
MonotonicityNot monotonic
2025-07-09T07:15:01.431252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 18
 
3.6%
25 17
 
3.4%
32 17
 
3.4%
23 15
 
3.0%
31 15
 
3.0%
34 15
 
3.0%
7 14
 
2.8%
28 14
 
2.8%
1 14
 
2.8%
21 13
 
2.6%
Other values (49) 348
69.6%
ValueCountFrequency (%)
0 13
2.6%
1 14
2.8%
2 13
2.6%
3 10
2.0%
4 10
2.0%
5 10
2.0%
6 12
2.4%
7 14
2.8%
8 10
2.0%
9 2
 
0.4%
ValueCountFrequency (%)
59 4
0.8%
58 1
 
0.2%
57 1
 
0.2%
56 1
 
0.2%
54 4
0.8%
53 4
0.8%
52 5
1.0%
51 4
0.8%
50 2
 
0.4%
49 2
 
0.4%

Absenteeism_Training_Hours
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.326
Minimum0
Maximum7
Zeros70
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-09T07:15:01.538410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2649468
Coefficient of variation (CV)0.6809822
Kurtosis-1.1857226
Mean3.326
Median Absolute Deviation (MAD)2
Skewness0.074831165
Sum1663
Variance5.129984
MonotonicityNot monotonic
2025-07-09T07:15:01.624008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 79
15.8%
0 70
14.0%
1 68
13.6%
3 63
12.6%
6 58
11.6%
2 58
11.6%
7 53
10.6%
5 51
10.2%
ValueCountFrequency (%)
0 70
14.0%
1 68
13.6%
2 58
11.6%
3 63
12.6%
4 79
15.8%
5 51
10.2%
6 58
11.6%
7 53
10.6%
ValueCountFrequency (%)
7 53
10.6%
6 58
11.6%
5 51
10.2%
4 79
15.8%
3 63
12.6%
2 58
11.6%
1 68
13.6%
0 70
14.0%

Attrition_Status
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
376 
True
124 
ValueCountFrequency (%)
False 376
75.2%
True 124
 
24.8%
2025-07-09T07:15:01.698400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-07-09T07:14:50.139105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.238595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.759615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.659298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.524840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.833773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.282188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.923649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.332133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.631809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.256492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.010875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.768750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:50.260333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.365704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.873049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.813563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.618801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.943924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.382402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.026842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.420477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.733648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.368922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.160510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.873863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:50.364696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.465383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.015781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.988273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.715452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.082324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.480638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.146135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.514142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.834879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.467685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.311636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.976328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:50.836140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.566803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.204876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.122687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.814243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.180015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.815290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.242121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.607767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.933361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.562535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.466737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.085180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:50.935847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.666530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.348203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.279125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.900737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.291557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.928040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.345998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.698853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:44.320709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.700583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.614019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.198338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.051002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.796028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.503292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.442859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.017479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.398885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.032407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.449265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.812842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:44.422578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.863298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:47.780583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.308298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.153774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.898417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.648506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.596790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:41.554288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.909339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:44.518007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:46.011341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:49.414273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.282892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:31.996751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.806009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.695832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.225235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.611472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.264814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.664785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.005922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:44.624095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:46.176987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.097468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.516084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.376000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.086650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:33.948200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.808345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.314678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.713968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.369580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:46.310776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.252087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.627695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.479993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.200095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.104809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:35.913929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.421675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:40.476871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:43.233540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:44.818230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:46.468750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.352944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.723615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.580423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.297394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.236296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.229251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.518365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:38.948275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.577426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:41.980535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:51.699419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.544074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.377467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.333132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.617537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.071244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.689183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:43.434876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T07:14:46.733639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.566469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:49.932427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:51.809617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:32.638971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:34.512414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:36.430950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:37.724648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:39.175704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:40.804977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:42.218531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:43.529351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:45.157533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:46.871472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:48.663224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T07:14:50.033260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-09T07:15:01.810841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Absenteeism_DaysAbsenteeism_Training_HoursAbsenteeism_vs_CompanyAgeAttended_Training_HoursAttrition_StatusAverage_Monthly_Work_HoursBranchBurnout_RiskDepartmentEmployee_IDEngagement_ChangeEngagement_ScoreEngagement_vs_CompanyGenderJob_RolePerformance_RatingPerformance_vs_CompanyPromotion_AttainedReason_for_LeavingSalary_LevelSatisfaction_LevelStill_EmployedTenure_at_CompanyTotal_Training_HoursWork_LocationYearly_EngagementYearly_Satisfaction_FeedbackYearly_Work_Hours
Absenteeism_Days1.000-0.0811.000-0.0160.0500.000-0.0640.0500.0000.056-0.010-0.0630.0540.0540.0000.0710.0000.0000.0000.0000.0700.0520.0000.0000.0450.0000.0000.000-0.064
Absenteeism_Training_Hours-0.0811.000-0.081-0.0350.0330.000-0.0460.0000.0000.000-0.018-0.029-0.017-0.0170.0000.0210.0820.0820.0000.0000.0470.0830.0000.0660.0340.0000.0040.020-0.046
Absenteeism_vs_Company1.000-0.0811.000-0.0160.0500.000-0.0640.0500.0000.056-0.010-0.0630.0540.0540.0000.0710.0000.0000.0000.0000.0700.0520.0000.0000.0450.0000.0000.000-0.064
Age-0.016-0.035-0.0161.0000.0360.0970.0120.0000.0000.0470.018-0.004-0.028-0.0280.0000.0820.0470.0470.1140.0860.0090.0320.0970.0290.0050.0000.0330.0000.012
Attended_Training_Hours0.0500.0330.0500.0361.0000.0640.0010.0730.0500.0000.0240.002-0.043-0.0430.0000.0820.0000.0000.0000.0620.0470.0560.0640.0000.3180.0000.0000.0550.001
Attrition_Status0.0000.0000.0000.0970.0641.0000.0000.0230.0000.0000.1160.0710.0000.0000.0470.0000.0100.0100.0000.9950.0000.0000.9950.9770.0000.0000.0850.0000.000
Average_Monthly_Work_Hours-0.064-0.046-0.0640.0120.0010.0001.0000.0740.8850.0610.0070.006-0.039-0.0390.0000.1170.0620.0620.0000.0680.0500.0300.0000.060-0.0680.0000.0000.0461.000
Branch0.0500.0000.0500.0000.0730.0230.0741.0000.0170.0740.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0230.0000.0100.0460.0000.0000.074
Burnout_Risk0.0000.0000.0000.0000.0500.0000.8850.0171.0000.0370.0000.0450.0000.0000.0590.0000.0290.0290.0000.0000.0420.0570.0000.0530.0760.0000.0000.0910.885
Department0.0560.0000.0560.0470.0000.0000.0610.0740.0371.0000.0560.0000.0000.0000.0000.0690.0190.0190.0000.0000.0780.0000.0000.0690.0550.0920.0000.0000.061
Employee_ID-0.010-0.018-0.0100.0180.0240.1160.0070.0000.0000.0561.0000.001-0.009-0.0090.0000.0140.0000.0000.0000.0360.0000.0300.1160.0450.0440.0000.0610.0000.007
Engagement_Change-0.063-0.029-0.063-0.0040.0020.0710.0060.0000.0450.0000.0011.000-0.215-0.2150.0000.0560.0430.0430.1130.0000.026-0.0870.0710.041-0.0180.0000.9920.0000.006
Engagement_Score0.054-0.0170.054-0.028-0.0430.000-0.0390.0000.0000.000-0.009-0.2151.0001.0000.0520.0510.0000.0000.0000.0260.0370.0160.0000.024-0.0220.0200.0000.000-0.039
Engagement_vs_Company0.054-0.0170.054-0.028-0.0430.000-0.0390.0000.0000.000-0.009-0.2151.0001.0000.0520.0510.0000.0000.0000.0260.0370.0160.0000.024-0.0220.0200.0000.000-0.039
Gender0.0000.0000.0000.0000.0000.0470.0000.0420.0590.0000.0000.0000.0520.0521.0000.0000.0000.0000.0000.0520.0000.0190.0470.1200.0000.0000.0000.0630.000
Job_Role0.0710.0210.0710.0820.0820.0000.1170.0000.0000.0690.0140.0560.0510.0510.0001.0000.0480.0480.0000.0000.9980.0000.0000.0000.0000.0490.0080.0000.117
Performance_Rating0.0000.0820.0000.0470.0000.0100.0620.0000.0290.0190.0000.0430.0000.0000.0000.0481.0001.0000.0000.0490.0870.0000.0100.0460.0000.0000.0430.0450.062
Performance_vs_Company0.0000.0820.0000.0470.0000.0100.0620.0000.0290.0190.0000.0430.0000.0000.0000.0481.0001.0000.0000.0490.0870.0000.0100.0460.0000.0000.0430.0450.062
Promotion_Attained0.0000.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.0000.0001.0000.0000.0000.0980.0000.1340.0000.0000.0000.0000.000
Reason_for_Leaving0.0000.0000.0000.0860.0620.9950.0680.0000.0000.0000.0360.0000.0260.0260.0520.0000.0490.0490.0001.0000.0000.0210.9950.3720.0000.0370.0260.0000.068
Salary_Level0.0700.0470.0700.0090.0470.0000.0500.0000.0420.0780.0000.0260.0370.0370.0000.9980.0870.0870.0000.0001.0000.0000.0000.0000.0480.0000.0270.0000.050
Satisfaction_Level0.0520.0830.0520.0320.0560.0000.0300.0000.0570.0000.030-0.0870.0160.0160.0190.0000.0000.0000.0980.0210.0001.0000.0000.0000.0380.0000.0710.0690.030
Still_Employed0.0000.0000.0000.0970.0640.9950.0000.0230.0000.0000.1160.0710.0000.0000.0470.0000.0100.0100.0000.9950.0000.0001.0000.9770.0000.0000.0850.0000.000
Tenure_at_Company0.0000.0660.0000.0290.0000.9770.0600.0000.0530.0690.0450.0410.0240.0240.1200.0000.0460.0460.1340.3720.0000.0000.9771.0000.0000.0800.0600.0810.060
Total_Training_Hours0.0450.0340.0450.0050.3180.000-0.0680.0100.0760.0550.044-0.018-0.022-0.0220.0000.0000.0000.0000.0000.0000.0480.0380.0000.0001.0000.0000.0000.027-0.068
Work_Location0.0000.0000.0000.0000.0000.0000.0000.0460.0000.0920.0000.0000.0200.0200.0000.0490.0000.0000.0000.0370.0000.0000.0000.0800.0001.0000.0000.0000.000
Yearly_Engagement0.0000.0040.0000.0330.0000.0850.0000.0000.0000.0000.0610.9920.0000.0000.0000.0080.0430.0430.0000.0260.0270.0710.0850.0600.0000.0001.0000.0000.000
Yearly_Satisfaction_Feedback0.0000.0200.0000.0000.0550.0000.0460.0000.0910.0000.0000.0000.0000.0000.0630.0000.0450.0450.0000.0000.0000.0690.0000.0810.0270.0000.0001.0000.046
Yearly_Work_Hours-0.064-0.046-0.0640.0120.0010.0001.0000.0740.8850.0610.0070.006-0.039-0.0390.0000.1170.0620.0620.0000.0680.0500.0300.0000.060-0.0680.0000.0000.0461.000

Missing values

2025-07-09T07:14:52.032585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-09T07:14:52.329669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Employee_IDEmploye_NameGenderAgeDate_of_BirthBranchDepartmentJob_RoleSalary_LevelWork_LocationJoining_DateLeaving_DateTenure_at_CompanyPromotion_AttainedStill_EmployedReason_for_LeavingSatisfaction_LevelEngagement_ScorePerformance_RatingAverage_Monthly_Work_HoursAbsenteeism_DaysBurnout_RiskYearly_Satisfaction_FeedbackYearly_EngagementYearly_Work_HoursEngagement_vs_CompanyAbsenteeism_vs_CompanyPerformance_vs_CompanyEngagement_ChangeTotal_Training_HoursAttended_Training_HoursAbsenteeism_Training_HoursAttrition_Status
01Miss Bertha BradtkeFemale5010/16/1974United_KingdomSupportEngineerMediumWork From Home3/19/2014NaN10YesYes0.600.16319410Low542328-0.33354-5.1680.0423.8450283No
12Dixie ErdmanMale453/3/1980SingaporeTechnicalAnalystMediumRemote2/8/2002NaN22NoYes0.720.9421400Low4216800.44646-15.168-0.9581.0645414No
23Andrea LangworthFemale431/11/1981IndiaTechnicalExecutiveHighOn Site12/14/2007NaN17YesYes0.290.61213429Low3416080.1164613.832-0.9583.3935323No
34Wendell BartonFemale243/9/2000South_AfricaTechnicalManagerHighOn Site11/16/2016NaN8NoYes0.450.01117913Low222148-0.48354-2.168-1.9581.9950101No
45Nicholas KuhnFemale307/28/1994United_KingdomSalesClerkLowWork From Home4/12/201912/21/20212NoNoHealth Issues0.680.44412714Low311524-0.05354-1.1681.0420.563065Yes
56Jodi GoyetteFemale575/26/1968SingaporeSalesAnalystMediumWork From Home5/31/20087/23/20102YesNoWork Environment0.430.21417328Low222076-0.2835412.8321.0421.7955364Yes
67Penny ConsidineMale5512/25/1969IndiaITManagerHighRemote6/12/2013NaN11NoYes0.330.57415920Low5219080.076464.8321.0421.435537No
78Dr. Sidney MosciskiMale458/12/1979South_AfricaSalesExecutiveHighWork From Home9/26/2019NaN5YesYes0.300.1711908Low142280-0.32354-7.168-1.9583.8360382No
89Vicky WolffFemale296/5/1996United_KingdomManagementExecutiveHighOn Site5/20/201512/13/20150YesNoLow Pay0.710.7312424High2229040.23646-11.168-1.9581.2745200Yes
910Jeanne KrisFemale492/29/1976SingaporeSalesEngineerMediumRemote4/25/2016NaN8YesYes0.450.10521811Medium332616-0.39354-4.1682.0422.9060571No
Employee_IDEmploye_NameGenderAgeDate_of_BirthBranchDepartmentJob_RoleSalary_LevelWork_LocationJoining_DateLeaving_DateTenure_at_CompanyPromotion_AttainedStill_EmployedReason_for_LeavingSatisfaction_LevelEngagement_ScorePerformance_RatingAverage_Monthly_Work_HoursAbsenteeism_DaysBurnout_RiskYearly_Satisfaction_FeedbackYearly_EngagementYearly_Work_HoursEngagement_vs_CompanyAbsenteeism_vs_CompanyPerformance_vs_CompanyEngagement_ChangeTotal_Training_HoursAttended_Training_HoursAbsenteeism_Training_HoursAttrition_Status
490491Jeanne KuhicMale336/2/1992IndiaTechnicalManagerHighRemote6/2/2016NaN8NoYes0.340.94516520Low1519800.446464.8322.0424.0645251No
491492Ronald Hintz PhDMale463/17/1979South_AfricaTechnicalEngineerMediumWork From Home12/3/2005NaN19YesYes0.680.40112613Low231512-0.09354-2.168-1.9582.6050243No
492493Darryl KassulkeFemale4911/12/1975United_KingdomManagementAnalystMediumWork From Home3/1/20026/27/20042NoNoHealth Issues0.320.58114524Low5317400.086468.832-1.9582.4255380Yes
493494Hazel HalvorsonFemale541/2/1971SingaporeTechnicalAnalystMediumOn Site7/9/2006NaN18NoYes0.060.50321212Medium1325440.00646-3.1680.0422.5060163No
494495Bonnie Trantow IMale591/4/1966IndiaTechnicalClerkLowRemote8/24/20087/11/20090YesNoWork Environment0.410.7311939Low1523160.23646-6.168-1.9584.276070Yes
495496Marc FeilMale5911/9/1965South_AfricaSupportExecutiveHighRemote1/4/20073/18/20081YesNoWork Environment0.160.68425110High2430120.18646-5.1681.0423.3235213Yes
496497Dwayne MosciskiMale224/27/2003United_KingdomManagementClerkLowOn Site2/16/2018NaN6NoYes0.360.10515517Low431860-0.393541.8322.0422.9035144No
497498Ms. Shelly HyattMale4811/6/1977SingaporeManagementEngineerMediumOn Site3/30/2012NaN12NoYes0.400.30316626Low341992-0.1935410.8320.0423.7035252No
498499Dr. Rafael JakubowskiFemale586/29/1967IndiaFinanceClerkLowRemote9/24/20195/6/20211YesNoHealth Issues0.500.20318511Low442220-0.29354-4.1680.0423.805583Yes
499500Rosalie Fadel MDMale3912/4/1986South_AfricaSupportClerkLowWork From Home7/6/2017NaN7NoYes0.120.8822535High4330360.38646-10.168-0.9582.1250190No